[2] Ackerman, B.:
The rise of world constitutionalism. Virginia Law Rev. 83 (1997), 771-797.
DOI 10.2307/1073748
[3] Alesina, A., Spolaore, E.:
On the number and size of nations. Quarterly J. Econom. 112 (1997), 1027-1056.
DOI 10.1162/003355300555411
[5] Bolton, P., Roland, G.:
The breakup of nations: A political economy analysis. Quarterly J. Econom. 112 (1997), 1057-1090.
DOI 10.1162/003355300555420
[6] Cerniglia, F.:
Decentralization in the public sector: quantitative aspects in federal and unitary countries. J. Pol. Model. 25 (2003), 749-776.
DOI 10.1016/s0161-8938(03)00069-3
[7] Choi, H., Reimherr, M.:
A geometric approach to confidence regions and bands for functional parameters. J. Royal Statist. Soc.: Series B (Statist. Methodology) 80 (2018), 239-260.
DOI 10.1111/rssb.12239 |
MR 3744720
[8] Cox, D. D., Lee, J. S.:
Pointwise testing with functional data using the Westfall-Young randomization method. Biometrika 95 (2008), 621-634.
DOI 10.1093/biomet/asn021 |
MR 2443179
[11] Ermini, B., santolini, R.:
does globalization matter on fiscal decentralization? New evidence from the OECD. Global Econom. Rev. 43 (2014), 153-183.
DOI 10.1080/1226508x.2014.920240
[12] 2018, Eurostat: Government revenue, Expenditure and Main Aggregates (gov_10a_main).
[13] Febrero-Bande, M., Oviedo de la Fuente, M.:
Statistical computing in functional data analysis: The R package fda.usc. J. Statist. Software 51 (2012), 1-28.
DOI 10.18637/jss.v051.i04
[16] Górecki, T., Smaga, L.:
fdANOVA: Analysis of Variance for Univariate and Multivariate Functional Data, R package version 0.1.0, 2017.
MR 3953673
[19] Mrkvička, T., Myllymäki, M., Hahn, U.:
Multiple Monte Carlo testing, with applications in spatial point processes. Statist. Comput. 27 (2017), 1239-1255.
DOI 10.1007/s11222-016-9683-9 |
MR 3647095
[20] Myllymäki, M., Mrkvička, T.: GET: Global envelopes in R. arXiv:1911.06583 stat.ME, 2019.
[21] Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H., Hahn, U.:
Global envelope tests for spatial processes. J. Royal Statist. Soc. B 79 (2017), 381-404.
DOI 10.1111/rssb.12172 |
MR 3611751
[23] Nichols, T. E.., Holmes, E.:
Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain M 15 (2001), 1-25.
DOI 10.1002/hbm.1058
[24] Oates, Wallace, E., E:
Toward A second-generation theory of fiscal federalism. Int. Tax Public Finance 12 (2005), 349-373.
DOI 10.1007/s10797-005-1619-9
[25] Pantazis, D., Nichols, T. E., Baillet, S., Leahya, R. M.:
A comparison of random field theory and permutation methods for the statistical analysis of MEG data. Neuroi 25 (2005), 383-394.
DOI 10.1016/j.neuroimage.2004.09.040
[26] Pini, A., Vantini, S., Colosimo, B. M., Grasso, M.:
Domain-selective functional analysis of variance for supervised statistical profile monitoring of signal data. J. Royal Statist. Soc.: Series C (Appl. Statist.) 67 (2001), 55-81.
DOI 10.1111/rssc.12218 |
MR 3758755
[27] Team, R Core: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Vienna 2019.
[29] Rodrik, D.:
Why do more open economies have bigger governments?. J. Polit. Economy 106 (1998), 997-1032.
DOI 10.1086/250038
[30] Sedova, J., Lipovska, H., Fischer: Fiscal autonomy in the secessionist regions. In: Current Trends in Public Sector Research, Masaryk University, Brno 2017.
[31] Spahn, P. B.: Contract Federalims. Edward Elgar Publishing Limited, Book Section 7, Cheltenham 2015, pp. 144-160.
[33] Stegarescu, D.:
The effects of economic and political integration on fiscal decentralization: Evidence from OECD countries. Canadian J. Econom. / Revue Canadienne d'Economique 42 (2009), 694-718.
DOI 10.1111/j.1540-5982.2009.01524.x
[34] Vo, D. H.: New Fiscal Decentralization Indices. The University of Western Australia Discussion Paper 08.14, 93, 2008.
[36] Vsevolozhskaya, O., Greenwood, M., Holodov, D.:
Pairwise comparison of treatment levels in functional analysis of variance with application to erythrocyte hemolysis. Ann. Appl. Statist. 8 (2014), 905-925.
DOI 10.1214/14-aoas723 |
MR 3262539
[37] Zhang, J.-T.:
Analysis of Variance for the functional data. Chapman and Hall, 2014.
DOI 10.1201/b15005